IDEAS home Printed from https://ideas.repec.org/a/wsi/acsxxx/v12y2009i02ns0219525909002143.html
   My bibliography  Save this article

On The Generative Nature Of Prediction

Author

Listed:
  • WOLFGANG LÖHR

    (Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany)

  • NIHAT AY

    (Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany;
    Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA)

Abstract

Given an observed stochastic process, computational mechanics provides an explicit and efficient method of constructing a minimal hidden Markov model within the class of maximally predictive models. Here, the corresponding so-called ε-machine encodes the mechanisms of prediction. We propose an alternative notion of predictive models in terms of a hidden Markov model capable of generating the underlying stochastic process. A comparison of these two notions of prediction reveals that our approach is less restrictive and thereby allows for predictive models that are more concise than the ε-machine.

Suggested Citation

  • Wolfgang Löhr & Nihat Ay, 2009. "On The Generative Nature Of Prediction," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 12(02), pages 169-194.
    • Handle: RePEc:wsi:acsxxx:v:12:y:2009:i:02:n:s0219525909002143
    DOI: 10.1142/S0219525909002143
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219525909002143
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219525909002143?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:acsxxx:v:12:y:2009:i:02:n:s0219525909002143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.